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Contents:

A foodscape is a socio-economic arrangement of a food system, placed in the biophysical context of a landscape. This is the scale and interdisciplinary level at which sustainability can be effectively assessed and managed. A foodscape is characterised by, inter alia, its soils, topography, climate, rivers, land use, fields, farms, crops, livestock, vegetation, habitats, residents, farmers, land owners, retailers, consumers, policies, legislation, communication and governance structures and their mutual interdependencies. Each foodscape is unique; it is a reflection of local bio-physical conditions, as well as cultural and socio-economic conditions. Some foodscapes are exemplars of sustainable food production, while others may be evolving towards or away from a sustainable future.

In the face of this complexity, how can science inform the transition of foodscapes towards environmental, economic and social sustainability? Solutions that make sense from a biological or geographical perspective may be wholly inappropriate within the local cultural or socio-economic context, or vice versa: social innovations may not be suitable given local biophysical constraints. Therefore, transitions towards sustainability require a comprehensive, interdisciplinary diagnosis of individual foodscapes.In this course, we will teach students principles and applications of a range of approaches to analyse foodscapes as systems through multiple lenses, with a portfolio of scientific methods. Rather than competing with each other, these methods are complementary and – together – can be used to diagnose foodscapes, show different perspectives, guide planning and decision-making for interventions and visualise transition pathways. Examples of the foodscape analysis approaches include:- fuzzy cognitive mapping (FCM), to map external and internal drivers and interactions in foodscapes;- sampling procedures for surveys and experimental design in communities and fields/landscapes;- statistical typologies and Q-methodology, for the characterisation of diversity and heterogeneity in foodscapes;- social and Ecological Network Analysis (SNA and ENA) and Signal mapping, to analyse social and ecological systems as networks;- pareto-based multi-objective optimisation with evolutionary algorithms applied to inverse modelling and farm/landscape models to explore trade-offs in decision making;- functional land management and public-private benefits framework, to match demand and supply of ecosystem services in foodscapes;- agent Based Modelling (ABM) to simulate interactions and dynamics in social-ecological systems.

We will use the International Network of Lighthouse Farms (Creating a global outdoor classroom and laboratory on sustainable foodscapes: https://www.wur.nl/en/Research-Results/Chair-groups/Plant-Sciences/Farming-Systems-Ecology-Group/Lighthouse-project.htm ) as our real-life case studies. We will bring these lighthouses to life, inside the classroom, using both Virtual Reality and Augmented Reality.

Learning outcomes:

After successful completion of this course, the students are able to:- use and apply a diverse set of methodologies for reading foodscapes;- discover key dimensions (the physical environment, farming systems, ecosystem services, functional land management, community dynamics, governance) of foodscapes and their interdependencies;- research the internal and external drivers (physical, legislative, economic, motivational, social) of the current development trajectory of a particular foodscape;- analyse the dynamics of combined social and ecological dimensions with a diverse set of methods and tools;- guide stakeholders in establishing and prioritising key objectives for the redesign and transition of a particular foodscape;- inform planning processes and support decision-making regarding entry points for reframing the key dimensions of a particular foodscape with a view to meeting the key objectives.

Activities:

Lectures, Tutorials, Case-studies, Group work, Practica (Modelling). Please note that there will also be on-site activities in week 7 (study week).